Multiple platform build/check report for BioC 3.21 - CHECK results for singleCellTK on merida1

Hostname	OS	Arch (*)	R version	Installed pkgs
nebbiolo1	Linux (Ubuntu 24.04.2 LTS)	x86_64	4.5.1 (2025-06-13) -- "Great Square Root"	4823
palomino7	Windows Server 2022 Datacenter	x64	4.5.1 (2025-06-13 ucrt) -- "Great Square Root"	4565
merida1	macOS 12.7.5 Monterey	x86_64	4.5.1 RC (2025-06-05 r88288) -- "Great Square Root"	4603
kjohnson1	macOS 13.6.6 Ventura	arm64	4.5.1 Patched (2025-06-14 r88325) -- "Great Square Root"	4544
kunpeng2	Linux (openEuler 24.03 LTS)	aarch64	R Under development (unstable) (2025-02-19 r87757) -- "Unsuffered Consequences"	4579
Click on any hostname to see more info about the system (e.g. compilers) () as reported by 'uname -p', except on Windows and Mac OS X*

CHECK results for singleCellTK on merida1

To the developers/maintainers of the singleCellTK package:
- Allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/singleCellTK.git to reflect on this report. See Troubleshooting Build Report for more information.
- Use the following Renviron settings to reproduce errors and warnings.
- If 'R CMD check' started to fail recently on the Linux builder(s) over a missing dependency, add the missing dependency to 'Suggests:' in your DESCRIPTION file. See Renviron.bioc for more information.

raw results

Summary

Package: singleCellTK

Version: 2.18.1

Command: /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings singleCellTK_2.18.1.tar.gz

StartedAt: 2025-08-08 10:15:33 -0400 (Fri, 08 Aug 2025)

EndedAt: 2025-08-08 10:46:17 -0400 (Fri, 08 Aug 2025)

EllapsedTime: 1844.8 seconds

RetCode: 0

Status: OK

CheckDir: singleCellTK.Rcheck

Warnings: 0

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:singleCellTK.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings singleCellTK_2.18.1.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/Users/biocbuild/bbs-3.21-bioc/meat/singleCellTK.Rcheck’
* using R version 4.5.1 RC (2025-06-05 r88288)
* using platform: x86_64-apple-darwin20
* R was compiled by
    Apple clang version 14.0.0 (clang-1400.0.29.202)
    GNU Fortran (GCC) 14.2.0
* running under: macOS Monterey 12.7.6
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘singleCellTK/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘singleCellTK’ version ‘2.18.1’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... INFO
Imports includes 79 non-default packages.
Importing from so many packages makes the package vulnerable to any of
them becoming unavailable.  Move as many as possible to Suggests and
use conditionally.
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘singleCellTK’ can be installed ... OK
* checking installed package size ... INFO
  installed size is  6.8Mb
  sub-directories of 1Mb or more:
    R         1.0Mb
    extdata   1.5Mb
    shiny     2.9Mb
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking whether startup messages can be suppressed ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... NOTE
Found the following Rd file(s) with Rd \link{} targets missing package
anchors:
  dedupRowNames.Rd: SingleCellExperiment-class
  detectCellOutlier.Rd: colData
  diffAbundanceFET.Rd: colData
  downSampleCells.Rd: SingleCellExperiment-class
  downSampleDepth.Rd: SingleCellExperiment-class
  featureIndex.Rd: SummarizedExperiment-class,
    SingleCellExperiment-class
  getBiomarker.Rd: SingleCellExperiment-class
  getDEGTopTable.Rd: SingleCellExperiment-class
  getEnrichRResult.Rd: SingleCellExperiment-class
  getFindMarkerTopTable.Rd: SingleCellExperiment-class
  getGenesetNamesFromCollection.Rd: SingleCellExperiment-class
  getPathwayResultNames.Rd: SingleCellExperiment-class
  getSampleSummaryStatsTable.Rd: SingleCellExperiment-class, assay,
    colData
  getSoupX.Rd: SingleCellExperiment-class
  getTSCANResults.Rd: SingleCellExperiment-class
  getTopHVG.Rd: SingleCellExperiment-class
  importAlevin.Rd: DelayedArray, readMM
  importAnnData.Rd: DelayedArray, readMM
  importBUStools.Rd: readMM
  importCellRanger.Rd: readMM, DelayedArray
  importCellRangerV2Sample.Rd: readMM, DelayedArray
  importCellRangerV3Sample.Rd: readMM, DelayedArray
  importDropEst.Rd: DelayedArray, readMM
  importExampleData.Rd: scRNAseq, Matrix, DelayedArray,
    ReprocessedFluidigmData, ReprocessedAllenData, NestorowaHSCData
  importFromFiles.Rd: readMM, DelayedArray, SingleCellExperiment-class
  importGeneSetsFromCollection.Rd: GeneSetCollection-class,
    SingleCellExperiment-class, GeneSetCollection, GSEABase, metadata
  importGeneSetsFromGMT.Rd: GeneSetCollection-class,
    SingleCellExperiment-class, getGmt, GSEABase, metadata
  importGeneSetsFromList.Rd: GeneSetCollection-class,
    SingleCellExperiment-class, GSEABase, metadata
  importGeneSetsFromMSigDB.Rd: SingleCellExperiment-class, msigdbr,
    GeneSetCollection-class, GSEABase, metadata
  importMitoGeneSet.Rd: SingleCellExperiment-class,
    GeneSetCollection-class, GSEABase, metadata
  importMultipleSources.Rd: DelayedArray
  importOptimus.Rd: readMM, DelayedArray
  importSEQC.Rd: readMM, DelayedArray
  importSTARsolo.Rd: readMM, DelayedArray
  iterateSimulations.Rd: SingleCellExperiment-class
  listSampleSummaryStatsTables.Rd: SingleCellExperiment-class, metadata
  plotBarcodeRankDropsResults.Rd: SingleCellExperiment-class
  plotBarcodeRankScatter.Rd: SingleCellExperiment-class
  plotBatchCorrCompare.Rd: SingleCellExperiment-class
  plotBatchVariance.Rd: SingleCellExperiment-class
  plotBcdsResults.Rd: SingleCellExperiment-class
  plotClusterAbundance.Rd: colData
  plotCxdsResults.Rd: SingleCellExperiment-class
  plotDEGHeatmap.Rd: SingleCellExperiment-class
  plotDEGRegression.Rd: SingleCellExperiment-class
  plotDEGViolin.Rd: SingleCellExperiment-class
  plotDEGVolcano.Rd: SingleCellExperiment-class
  plotDecontXResults.Rd: SingleCellExperiment-class
  plotDoubletFinderResults.Rd: SingleCellExperiment-class
  plotEmptyDropsResults.Rd: SingleCellExperiment-class
  plotEmptyDropsScatter.Rd: SingleCellExperiment-class
  plotFindMarkerHeatmap.Rd: SingleCellExperiment-class
  plotPCA.Rd: SingleCellExperiment-class
  plotPathway.Rd: SingleCellExperiment-class
  plotRunPerCellQCResults.Rd: SingleCellExperiment-class
  plotSCEBarAssayData.Rd: SingleCellExperiment-class
  plotSCEBarColData.Rd: SingleCellExperiment-class
  plotSCEBatchFeatureMean.Rd: SingleCellExperiment-class
  plotSCEDensity.Rd: SingleCellExperiment-class
  plotSCEDensityAssayData.Rd: SingleCellExperiment-class
  plotSCEDensityColData.Rd: SingleCellExperiment-class
  plotSCEDimReduceColData.Rd: SingleCellExperiment-class
  plotSCEDimReduceFeatures.Rd: SingleCellExperiment-class
  plotSCEHeatmap.Rd: SingleCellExperiment-class
  plotSCEScatter.Rd: SingleCellExperiment-class
  plotSCEViolin.Rd: SingleCellExperiment-class
  plotSCEViolinAssayData.Rd: SingleCellExperiment-class
  plotSCEViolinColData.Rd: SingleCellExperiment-class
  plotScDblFinderResults.Rd: SingleCellExperiment-class
  plotScdsHybridResults.Rd: SingleCellExperiment-class
  plotScrubletResults.Rd: SingleCellExperiment-class
  plotSoupXResults.Rd: SingleCellExperiment-class
  plotTSCANClusterDEG.Rd: SingleCellExperiment-class
  plotTSCANClusterPseudo.Rd: SingleCellExperiment-class
  plotTSCANDimReduceFeatures.Rd: SingleCellExperiment-class
  plotTSCANPseudotimeGenes.Rd: SingleCellExperiment-class
  plotTSCANPseudotimeHeatmap.Rd: SingleCellExperiment-class
  plotTSCANResults.Rd: SingleCellExperiment-class
  plotTSNE.Rd: SingleCellExperiment-class
  plotUMAP.Rd: SingleCellExperiment-class
  readSingleCellMatrix.Rd: DelayedArray
  reportCellQC.Rd: SingleCellExperiment-class
  reportClusterAbundance.Rd: colData
  reportDiffAbundanceFET.Rd: colData
  retrieveSCEIndex.Rd: SingleCellExperiment-class
  runBBKNN.Rd: SingleCellExperiment-class
  runBarcodeRankDrops.Rd: SingleCellExperiment-class, colData
  runBcds.Rd: SingleCellExperiment-class, colData
  runCellQC.Rd: colData
  runComBatSeq.Rd: SingleCellExperiment-class
  runCxds.Rd: SingleCellExperiment-class, colData
  runCxdsBcdsHybrid.Rd: colData
  runDEAnalysis.Rd: SingleCellExperiment-class
  runDecontX.Rd: colData
  runDimReduce.Rd: SingleCellExperiment-class
  runDoubletFinder.Rd: SingleCellExperiment-class
  runDropletQC.Rd: colData
  runEmptyDrops.Rd: SingleCellExperiment-class, colData
  runEnrichR.Rd: SingleCellExperiment-class
  runFastMNN.Rd: SingleCellExperiment-class, BiocParallelParam-class
  runFeatureSelection.Rd: SingleCellExperiment-class
  runFindMarker.Rd: SingleCellExperiment-class
  runGSVA.Rd: SingleCellExperiment-class
  runHarmony.Rd: SingleCellExperiment-class
  runKMeans.Rd: SingleCellExperiment-class, colData
  runLimmaBC.Rd: SingleCellExperiment-class, assay
  runMNNCorrect.Rd: SingleCellExperiment-class, assay,
    BiocParallelParam-class
  runModelGeneVar.Rd: SingleCellExperiment-class
  runPerCellQC.Rd: SingleCellExperiment-class, BiocParallelParam,
    colData
  runSCANORAMA.Rd: SingleCellExperiment-class, assay
  runSCMerge.Rd: SingleCellExperiment-class, colData, assay,
    BiocParallelParam-class
  runScDblFinder.Rd: SingleCellExperiment-class, colData
  runScranSNN.Rd: SingleCellExperiment-class, reducedDim, assay,
    altExp, colData, igraph
  runScrublet.Rd: SingleCellExperiment-class, colData
  runSingleR.Rd: SingleCellExperiment-class
  runSoupX.Rd: SingleCellExperiment-class
  runTSCAN.Rd: SingleCellExperiment-class
  runTSCANClusterDEAnalysis.Rd: SingleCellExperiment-class
  runTSCANDEG.Rd: SingleCellExperiment-class
  runTSNE.Rd: SingleCellExperiment-class
  runUMAP.Rd: SingleCellExperiment-class, BiocParallelParam-class
  runVAM.Rd: SingleCellExperiment-class
  runZINBWaVE.Rd: SingleCellExperiment-class, colData,
    BiocParallelParam-class
  sampleSummaryStats.Rd: SingleCellExperiment-class, assay, colData
  scaterPCA.Rd: SingleCellExperiment-class, BiocParallelParam-class
  scaterlogNormCounts.Rd: logNormCounts
  sctkListGeneSetCollections.Rd: GeneSetCollection-class
  sctkPythonInstallConda.Rd: conda_install, reticulate, conda_create
  sctkPythonInstallVirtualEnv.Rd: virtualenv_install, reticulate,
    virtualenv_create
  selectSCTKConda.Rd: reticulate
  selectSCTKVirtualEnvironment.Rd: reticulate
  setRowNames.Rd: SingleCellExperiment-class
  setSCTKDisplayRow.Rd: SingleCellExperiment-class
  singleCellTK.Rd: SingleCellExperiment-class
  subsetSCECols.Rd: SingleCellExperiment-class
  subsetSCERows.Rd: SingleCellExperiment-class, altExp
  summarizeSCE.Rd: SingleCellExperiment-class
Please provide package anchors for all Rd \link{} targets not in the
package itself and the base packages.
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking R/sysdata.rda ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
                           user system elapsed
importGeneSetsFromMSigDB 90.948  0.929  93.592
plotDoubletFinderResults 53.155  0.252  55.563
plotScDblFinderResults   48.415  1.207  57.632
runDoubletFinder         45.208  0.199  47.746
runScDblFinder           33.989  0.480  35.662
importExampleData        23.105  2.191  26.154
plotBatchCorrCompare     17.614  0.154  18.789
plotScdsHybridResults    13.312  0.123  14.832
plotBcdsResults          12.995  0.203  13.362
plotDecontXResults       12.181  0.086  12.701
plotCxdsResults          10.332  0.074  10.481
plotFindMarkerHeatmap    10.166  0.063  10.981
plotEmptyDropsResults    10.064  0.043  10.705
plotDEGViolin             9.805  0.157  10.176
plotEmptyDropsScatter     9.902  0.045  10.350
runEmptyDrops             9.374  0.026   9.508
convertSCEToSeurat        8.903  0.422   9.533
runDecontX                9.270  0.055  10.032
plotTSCANClusterDEG       8.806  0.241  10.026
runUMAP                   8.834  0.086   9.032
plotUMAP                  8.834  0.084   9.627
plotDEGRegression         7.908  0.068   8.113
detectCellOutlier         7.519  0.165   7.787
runSeuratSCTransform      7.208  0.123   7.407
plotDEGHeatmap            5.403  0.096   5.606
plotRunPerCellQCResults   4.784  0.094   6.145
getEnrichRResult          0.686  0.051   9.070
runEnrichR                0.634  0.044   8.619
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘spelling.R’
  Running ‘testthat.R’
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

Status: 1 NOTE
See
  ‘/Users/biocbuild/bbs-3.21-bioc/meat/singleCellTK.Rcheck/00check.log’
for details.

Installation output

singleCellTK.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL singleCellTK
###
##############################################################################
##############################################################################


* installing to library ‘/Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/library’
* installing *source* package ‘singleCellTK’ ...
** this is package ‘singleCellTK’ version ‘2.18.1’
** using staged installation
** R
** data
** exec
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (singleCellTK)

Tests output

singleCellTK.Rcheck/tests/spelling.Rout


R version 4.5.1 RC (2025-06-05 r88288) -- "Great Square Root"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> if (requireNamespace('spelling', quietly = TRUE))
+   spelling::spell_check_test(vignettes = TRUE, error = FALSE, skip_on_cran = TRUE)
NULL
> 
> proc.time()
   user  system elapsed 
  0.348   0.116   0.469

singleCellTK.Rcheck/tests/testthat.Rout


R version 4.5.1 RC (2025-06-05 r88288) -- "Great Square Root"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(testthat)
> library(singleCellTK)
Loading required package: SummarizedExperiment
Loading required package: MatrixGenerics
Loading required package: matrixStats

Attaching package: 'MatrixGenerics'

The following objects are masked from 'package:matrixStats':

    colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
    colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
    colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
    colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
    colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
    colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
    colWeightedMeans, colWeightedMedians, colWeightedSds,
    colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
    rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
    rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
    rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
    rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
    rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
    rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
    rowWeightedSds, rowWeightedVars

Loading required package: GenomicRanges
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: generics

Attaching package: 'generics'

The following objects are masked from 'package:base':

    as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
    setequal, union


Attaching package: 'BiocGenerics'

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
    as.data.frame, basename, cbind, colnames, dirname, do.call,
    duplicated, eval, evalq, get, grep, grepl, is.unsorted, lapply,
    mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
    rank, rbind, rownames, sapply, saveRDS, table, tapply, unique,
    unsplit, which.max, which.min

Loading required package: S4Vectors

Attaching package: 'S4Vectors'

The following object is masked from 'package:utils':

    findMatches

The following objects are masked from 'package:base':

    I, expand.grid, unname

Loading required package: IRanges
Loading required package: GenomeInfoDb
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.


Attaching package: 'Biobase'

The following object is masked from 'package:MatrixGenerics':

    rowMedians

The following objects are masked from 'package:matrixStats':

    anyMissing, rowMedians

Loading required package: SingleCellExperiment
Loading required package: DelayedArray
Loading required package: Matrix

Attaching package: 'Matrix'

The following object is masked from 'package:S4Vectors':

    expand

Loading required package: S4Arrays
Loading required package: abind

Attaching package: 'S4Arrays'

The following object is masked from 'package:abind':

    abind

The following object is masked from 'package:base':

    rowsum

Loading required package: SparseArray

Attaching package: 'DelayedArray'

The following objects are masked from 'package:base':

    apply, scale, sweep


Attaching package: 'singleCellTK'

The following object is masked from 'package:BiocGenerics':

    plotPCA

> 
> test_check("singleCellTK")
Found 2 batches
Using null model in ComBat-seq.
Adjusting for 0 covariate(s) or covariate level(s)
Estimating dispersions
Fitting the GLM model
Shrinkage off - using GLM estimates for parameters
Adjusting the data
Found 2 batches
Using null model in ComBat-seq.
Adjusting for 1 covariate(s) or covariate level(s)
Estimating dispersions
Fitting the GLM model
Shrinkage off - using GLM estimates for parameters
Adjusting the data
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Uploading data to Enrichr... Done.
  Querying HDSigDB_Human_2021... Done.
Parsing results... Done.
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene means
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene means
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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  |======================================================================| 100%
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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  |======================================================================| 100%

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  |======================================================================| 100%
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 390
Number of edges: 9849

Running Louvain algorithm...
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.8351
Number of communities: 7
Elapsed time: 0 seconds
Using method 'umap'
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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  |======================================================================| 100%

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  |======================================================================| 100%
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

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  |======================================================================| 100%

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  |======================================================================| 100%

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  |======================================================================| 100%
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
[ FAIL 0 | WARN 22 | SKIP 0 | PASS 225 ]

[ FAIL 0 | WARN 22 | SKIP 0 | PASS 225 ]
> 
> proc.time()
   user  system elapsed 
517.930  10.376 556.153

Example timings

singleCellTK.Rcheck/singleCellTK-Ex.timings

name	user	system	elapsed
MitoGenes	0.004	0.005	0.010
SEG	0.004	0.005	0.009
calcEffectSizes	0.454	0.031	0.494
combineSCE	1.697	0.027	1.749
computeZScore	0.412	0.022	0.438
convertSCEToSeurat	8.903	0.422	9.533
convertSeuratToSCE	0.741	0.016	0.758
dedupRowNames	0.117	0.004	0.122
detectCellOutlier	7.519	0.165	7.787
diffAbundanceFET	0.097	0.008	0.106
discreteColorPalette	0.010	0.001	0.011
distinctColors	0.005	0.000	0.005
downSampleCells	1.062	0.106	1.181
downSampleDepth	0.892	0.049	0.947
expData-ANY-character-method	0.283	0.008	0.292
expData-set-ANY-character-CharacterOrNullOrMissing-logical-method	0.348	0.008	0.359
expData-set	0.334	0.007	0.346
expData	0.280	0.007	0.288
expDataNames-ANY-method	0.258	0.006	0.265
expDataNames	0.257	0.009	0.270
expDeleteDataTag	0.059	0.005	0.064
expSetDataTag	0.044	0.004	0.048
expTaggedData	0.047	0.005	0.052
exportSCE	0.040	0.006	0.045
exportSCEtoAnnData	0.119	0.009	0.131
exportSCEtoFlatFile	0.121	0.010	0.132
featureIndex	0.069	0.013	0.081
generateSimulatedData	0.094	0.010	0.105
getBiomarker	0.112	0.013	0.126
getDEGTopTable	1.618	0.124	1.766
getDiffAbundanceResults	0.087	0.005	0.093
getEnrichRResult	0.686	0.051	9.070
getFindMarkerTopTable	3.463	0.066	3.573
getMSigDBTable	0.008	0.007	0.014
getPathwayResultNames	0.039	0.006	0.045
getSampleSummaryStatsTable	0.466	0.007	0.478
getSoupX	0.000	0.001	0.000
getTSCANResults	2.241	0.059	2.320
getTopHVG	1.781	0.021	1.814
importAnnData	0.002	0.001	0.003
importBUStools	0.352	0.007	0.377
importCellRanger	1.711	0.053	1.861
importCellRangerV2Sample	0.335	0.004	0.341
importCellRangerV3Sample	0.691	0.023	0.741
importDropEst	0.458	0.007	0.468
importExampleData	23.105	2.191	26.154
importGeneSetsFromCollection	1.587	0.139	1.767
importGeneSetsFromGMT	0.135	0.008	0.152
importGeneSetsFromList	0.274	0.010	0.322
importGeneSetsFromMSigDB	90.948	0.929	93.592
importMitoGeneSet	0.107	0.015	0.125
importOptimus	0.003	0.002	0.004
importSEQC	0.324	0.026	0.352
importSTARsolo	0.368	0.042	0.417
iterateSimulations	0.367	0.039	0.407
listSampleSummaryStatsTables	0.540	0.061	0.603
mergeSCEColData	0.872	0.087	0.971
mouseBrainSubsetSCE	0.062	0.006	0.069
msigdb_table	0.002	0.005	0.008
plotBarcodeRankDropsResults	1.316	0.037	1.375
plotBarcodeRankScatter	1.569	0.021	1.614
plotBatchCorrCompare	17.614	0.154	18.789
plotBatchVariance	0.736	0.035	0.774
plotBcdsResults	12.995	0.203	13.362
plotBubble	1.660	0.058	1.728
plotClusterAbundance	2.089	0.015	2.168
plotCxdsResults	10.332	0.074	10.481
plotDEGHeatmap	5.403	0.096	5.606
plotDEGRegression	7.908	0.068	8.113
plotDEGViolin	9.805	0.157	10.176
plotDEGVolcano	1.821	0.018	1.858
plotDecontXResults	12.181	0.086	12.701
plotDimRed	0.409	0.007	0.417
plotDoubletFinderResults	53.155	0.252	55.563
plotEmptyDropsResults	10.064	0.043	10.705
plotEmptyDropsScatter	9.902	0.045	10.350
plotFindMarkerHeatmap	10.166	0.063	10.981
plotMASTThresholdGenes	3.379	0.085	4.345
plotPCA	0.738	0.026	0.956
plotPathway	1.405	0.042	1.992
plotRunPerCellQCResults	4.784	0.094	6.145
plotSCEBarAssayData	0.410	0.013	0.569
plotSCEBarColData	0.331	0.012	0.455
plotSCEBatchFeatureMean	0.545	0.012	0.690
plotSCEDensity	0.575	0.016	0.730
plotSCEDensityAssayData	0.372	0.012	0.448
plotSCEDensityColData	0.471	0.013	0.612
plotSCEDimReduceColData	1.179	0.020	1.418
plotSCEDimReduceFeatures	0.608	0.013	0.718
plotSCEHeatmap	1.093	0.013	1.398
plotSCEScatter	0.564	0.017	0.727
plotSCEViolin	0.576	0.016	0.738
plotSCEViolinAssayData	0.593	0.017	0.805
plotSCEViolinColData	0.566	0.019	0.732
plotScDblFinderResults	48.415	1.207	57.632
plotScanpyDotPlot	0.042	0.006	0.052
plotScanpyEmbedding	0.040	0.005	0.050
plotScanpyHVG	0.040	0.006	0.050
plotScanpyHeatmap	0.040	0.004	0.049
plotScanpyMarkerGenes	0.040	0.005	0.053
plotScanpyMarkerGenesDotPlot	0.039	0.005	0.047
plotScanpyMarkerGenesHeatmap	0.040	0.006	0.054
plotScanpyMarkerGenesMatrixPlot	0.041	0.006	0.051
plotScanpyMarkerGenesViolin	0.041	0.006	0.052
plotScanpyMatrixPlot	0.040	0.004	0.048
plotScanpyPCA	0.041	0.004	0.051
plotScanpyPCAGeneRanking	0.043	0.004	0.050
plotScanpyPCAVariance	0.039	0.007	0.052
plotScanpyViolin	0.040	0.004	0.051
plotScdsHybridResults	13.312	0.123	14.832
plotScrubletResults	0.039	0.005	0.046
plotSeuratElbow	0.041	0.005	0.049
plotSeuratHVG	0.043	0.005	0.050
plotSeuratJackStraw	0.038	0.004	0.048
plotSeuratReduction	0.038	0.004	0.046
plotSoupXResults	0.000	0.000	0.001
plotTSCANClusterDEG	8.806	0.241	10.026
plotTSCANClusterPseudo	2.844	0.035	3.127
plotTSCANDimReduceFeatures	2.921	0.038	3.294
plotTSCANPseudotimeGenes	3.286	0.033	3.579
plotTSCANPseudotimeHeatmap	3.035	0.035	3.227
plotTSCANResults	2.671	0.034	2.859
plotTSNE	0.694	0.015	0.758
plotTopHVG	1.159	0.026	1.263
plotUMAP	8.834	0.084	9.627
readSingleCellMatrix	0.010	0.002	0.012
reportCellQC	0.180	0.009	0.209
reportDropletQC	0.042	0.003	0.049
reportQCTool	0.189	0.008	0.212
retrieveSCEIndex	0.052	0.006	0.063
runBBKNN	0.001	0.001	0.001
runBarcodeRankDrops	0.501	0.011	0.563
runBcds	3.386	0.052	3.754
runCellQC	0.172	0.008	0.192
runClusterSummaryMetrics	0.876	0.015	0.937
runComBatSeq	1.000	0.023	1.096
runCxds	0.688	0.020	0.770
runCxdsBcdsHybrid	3.476	0.137	3.943
runDEAnalysis	0.969	0.071	1.142
runDecontX	9.270	0.055	10.032
runDimReduce	0.637	0.009	0.691
runDoubletFinder	45.208	0.199	47.746
runDropletQC	0.039	0.004	0.048
runEmptyDrops	9.374	0.026	9.508
runEnrichR	0.634	0.044	8.619
runFastMNN	4.013	0.070	4.128
runFeatureSelection	0.446	0.008	0.456
runFindMarker	3.276	0.057	3.387
runGSVA	1.510	0.062	1.580
runHarmony	0.210	0.013	0.243
runKMeans	0.371	0.012	0.393
runLimmaBC	0.180	0.002	0.183
runMNNCorrect	0.869	0.007	0.889
runModelGeneVar	0.663	0.009	0.686
runNormalization	3.423	0.042	3.504
runPerCellQC	0.723	0.014	0.742
runSCANORAMA	0.000	0.000	0.001
runSCMerge	0.007	0.001	0.008
runScDblFinder	33.989	0.480	35.662
runScanpyFindClusters	0.040	0.011	0.050
runScanpyFindHVG	0.042	0.007	0.050
runScanpyFindMarkers	0.040	0.005	0.045
runScanpyNormalizeData	0.218	0.006	0.225
runScanpyPCA	0.048	0.007	0.055
runScanpyScaleData	0.041	0.007	0.049
runScanpyTSNE	0.040	0.005	0.044
runScanpyUMAP	0.045	0.003	0.049
runScranSNN	0.645	0.017	0.665
runScrublet	0.039	0.006	0.051
runSeuratFindClusters	0.039	0.007	0.047
runSeuratFindHVG	1.062	0.015	1.095
runSeuratHeatmap	0.040	0.006	0.046
runSeuratICA	0.038	0.003	0.041
runSeuratJackStraw	0.040	0.008	0.049
runSeuratNormalizeData	0.041	0.005	0.046
runSeuratPCA	0.041	0.005	0.047
runSeuratSCTransform	7.208	0.123	7.407
runSeuratScaleData	0.038	0.005	0.045
runSeuratUMAP	0.038	0.004	0.043
runSingleR	0.084	0.005	0.088
runSoupX	0.000	0.001	0.001
runTSCAN	1.512	0.021	1.544
runTSCANClusterDEAnalysis	1.766	0.038	1.821
runTSCANDEG	1.702	0.038	1.759
runTSNE	1.304	0.015	1.338
runUMAP	8.834	0.086	9.032
runVAM	0.700	0.009	0.742
runZINBWaVE	0.007	0.002	0.008
sampleSummaryStats	0.356	0.006	0.370
scaterCPM	0.209	0.006	0.216
scaterPCA	1.023	0.011	1.041
scaterlogNormCounts	0.443	0.008	0.456
sce	0.038	0.010	0.048
sctkListGeneSetCollections	0.173	0.007	0.182
sctkPythonInstallConda	0.001	0.001	0.001
sctkPythonInstallVirtualEnv	0.000	0.000	0.001
selectSCTKConda	0.000	0.001	0.001
selectSCTKVirtualEnvironment	0.000	0.000	0.001
setRowNames	0.189	0.006	0.197
setSCTKDisplayRow	0.675	0.011	0.731
singleCellTK	0.001	0.001	0.001
subDiffEx	0.656	0.032	0.693
subsetSCECols	0.185	0.010	0.196
subsetSCERows	0.481	0.018	0.506
summarizeSCE	0.136	0.008	0.144
trimCounts	0.334	0.012	0.350